Update README.md

README.md

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----
-license: apache-2.0
----
+---
+base_model: microsoft/DialoGPT-small
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- conversational-ai
+- finance
+- fintech
+- trading
+- market-sentiment
+- financial-news
+- sentiment-analysis
+- lora
+- market-analysis
+- trading-assistant
+- investment-research
+- hedge-funds
+- algorithmic-trading
+language:
+- en
+license: mit
+datasets:
+- zeroshot/twitter-financial-news-sentiment
+metrics:
+- perplexity
+- accuracy
+widget:
+- text: "<|user|> What's the market sentiment for this news: Apple reports record quarterly earnings beating analyst expectations <|bot|>"
+  example_title: "Bullish News Analysis"
+- text: "<|user|> What's the market sentiment for this news: Tech stocks face regulatory pressure from new government policies <|bot|>"  
+  example_title: "Bearish News Analysis"
+- text: "<|user|> What's the market sentiment for this news: Federal Reserve maintains current interest rates as expected <|bot|>"
+  example_title: "Neutral Market News"
+---
+
+# DialoGPT-Financial-Market-Sentiment-Trading-Assistant
+
+Fine-tuned DialoGPT-small for financial news sentiment analysis and market sentiment interpretation for trading and investment decisions.
+
+## Overview
+- **Base Model:** microsoft/DialoGPT-small (117M parameters)
+- **Fine-tuning Method:** LoRA (4-bit quantization)
+- **Dataset:** Financial news sentiment dataset (1.5K samples)
+- **Training:** 3 epochs with optimized hyperparameters
+
+## Key Features
+- Real-time financial news sentiment analysis
+- Market sentiment interpretation for trading decisions
+- Bullish/Bearish/Neutral sentiment classification
+- Conversational interface for market analysis
+- Optimized for trading desks and investment research
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant")
+tokenizer = AutoTokenizer.from_pretrained("sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant")
+
+# Market sentiment analysis example
+prompt = "<|user|> What's the market sentiment for this news: Tesla stock surges after record delivery numbers <|bot|>"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=150, pad_token_id=tokenizer.eos_token_id)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Applications
+- Trading desk sentiment analysis
+- Algorithmic trading signal generation
+- Investment research and market analysis
+- Hedge fund market sentiment monitoring
+- Financial news interpretation for portfolio decisions
+
+## Training Details
+- LoRA rank: 8, alpha: 16
+- 4-bit NF4 quantization with fp16 precision
+- Learning rate: 3e-4 with linear scheduling
+- Batch size: 8, Max length: 256 tokens
+- 3 epochs on curated financial sentiment data
+
+Specialized for high-frequency market sentiment analysis in trading environments.